Search

Facing the problem of workforce and time costs caused by the rescheduling of large‐scale power grids with renewable energy penetration, and the lack of parallel control research on rotor angle instability and transient voltage instability, a rescheduling method based on trajectory sensitivity and deep reinforcement learning (DRL) is proposed to meet the requirements of rotor angle stability and transient voltage stability at the same time. By introducing the process of rescheduling to meet the transient stability constraint, the Markov decision‐making process of rescheduling is first constructed. Then, a simple dominant instability type identification method is proposed according to the occurrence time, location, voltage pattern, and position of the oscillation centre. Moreover, a rescheduling strategy is formulated based on the dominant instability mode identification, trajectory sensitivity calculation, actionable device selection, action amount computation, and action of the device. Next, according to the improved distributed distributive deep deterministic policy gradients (D4PG), a DRL model is established to map the action to actionable generator pairs and capacitors/reactors, so as to realize parallel rescheduling of rotor angle instability and transient voltage instability. Finally, the improved New England 39‐bus system and an actual power grid are used to verify the effectiveness and advantages of the method. [ABSTRACT FROM AUTHOR]

• Weak horizontal and vertical transmission channels are defined. • A power flow adjustment strategy that can fasten the adjustment processing is formulated. • The prior probability of action is given to improve learning efficiency. • A deep reinforcement learning model suitable for power flow adjustment is established. To solve the problem of human resources and time consumption caused by unsolvable power flow of weak power grid, an adjustment method for unsolvable power flow based on weak transmission channels and deep reinforcement learning (DRL) is proposed. Weak horizontal and vertical channels are defined, and their relationship with unsolvable power flow is demonstrated. Then, based on the idea of reducing the load level first and then gradually recovering it, actionable device and action margin are determined based on transmission channel positioning, adjustment sensitivity and solvability degree, so as to form a power flow adjustment strategy. Next, the Markov decision-making process of power flow adjustment is constructed, the prior probability of action is given by adjustment sensitivity of the actionable device, and the action is mapped to the adjustment amounts of generators and capacitors/reactors. Based on soft actor-critic, a DRL model suitable for power flow adjustment is established. Finally, the simulation results show that the proposed method performs well both in the New England 39-bus standard system and an actual power grid of China. Compared with the results obtained by other methods, the superiority of the proposed method is verified. [ABSTRACT FROM AUTHOR]

• A comprehensive evaluation on power flow solvability guides the search direction of learning. • Combining GCN and RNN to extract the spatial-temporal information makes the learned information more valuable. • Action space of reinforcement learning is narrowed according to knowledge. • For actual power system, transmission channel off-limit is a key factor for unsolvable problem. In view of the problem of manpower and time consumption caused by unsolvable power flow in current AC / DC hybrid power grid with new energy, a power flow state assessment and solvability recovery adjustment method based on graph convolutional recurrent neural network (GCRNN) and deep double Q-learning network (DDQN) is proposed. First, active power balance degree, reactive power balance degree and channel transmission margin are defined and weighted together to evaluate the solvability of power flow. Then, a power flow state evaluation model based on GCRNN is proposed by combining graph convolution with recurrent neural network, where the input is tangent vector of power flow equations, and the output is comprehensive evaluation index of solvability. Next, the state space, action space and multiple rewards of deep reinforcement learning are formed. By introducing the search direction, learning information extraction and search space constraints, an automatic adjustment method to restore power flow solvability is designed. Finally, the simulation results show that the proposed method performs well both in New England 39-bus standard system and actual power grid of China. Compared with the results obtained by other methods, the superiority of the proposed method is verified. [ABSTRACT FROM AUTHOR]